In the United States, Obesity, Type 2 Diabetes, and related metabolic diseases have reached epidemic proportions. A major hurdle to treating these diseases is our limited understanding of how genes function together and across different tissues to regulate whole-body metabolism. Methods in which a single gene is studied or manipulated to assess its function in overall metabolic health have often produced conflicting results. The function of most genes is highly contextual, i.e. dependent on the particular tissue and physiological state in which the gene is expressed. The emergence of high-throughput genomic technologies has provided new opportunities to study gene functions in the context of specific cell regulatory networks, and holds the potential for improved understanding of human disease. The goal of this proposal is to leverage genome-wide data on multiple master regulators of metabolic processes in order to understand the mechanisms by which these genes coordinate an appropriate cellular response to physiological stress. The focus of the first aim will be the liver, as it is central to the maintenance of important metabolites such as glucose and lipoproteins. While the individual functions of many regulatory genes in the liver have been studied using molecular and genetic methods, there remains a need for broader understanding of how these genes interact, and the functional consequences of these interactions on overall metabolic health. Specifically, multiple genomic profiles of protein-DNA interactions will be integrated to assess the extent of co-localization by proteins regulating metabolic gene expression, the genomic and epigenomic features impacting such co- localization, and the functional consequences of coordinated regulation by multiple factors. In the second aim, complementary genomic profiles from adipose tissue will be used to investigate the mechanisms by which these same genes can be active in multiple metabolic tissues yet affect distinct, even opposing, metabolic pathways. Central to these investigations will be the development of novel computational methods for integrating multiple genome-wide datasets to draw biological conclusions about the overall function of gene regulatory networks in the maintenance of organismal metabolic homeostasis. The results of these investigations will improve our interpretation of the current conflicting results from genetic manipulation of individual regulatory genes, as well as generate novel hypotheses about the mechanisms of metabolic disease progression. Ultimately, knowledge of the complex network of genes regulating metabolism will improve our ability to design safe and effective molecular interventions for metabolic diseases.